There are many examples where neural networks have been effectively used to predict protein secondary and tertiary structure from the primary sequence data. Here we describe the use of a Kohonen Self-Organizing Map (SOM) to categorise proteins based on secondary structure, and attempt to relate this information to functional data.

Among the large number of research publications discussing the SOM (Self-Organizing Map) [1, 2, 18, 19] different variants and extensions have been introduced. One of the SOM based models is the Growing Hierarchical Self-Organizing Map (GHSOM) [3-6]. The GHSOM is a neural architecture combining the advantages of two principal extensions of the self-organizing map, dynamic growth and hierarchica...

This paper introduces the DANTE project (Detection of Anomalies and Novelties in Time sEries with self-organizing networks), the goal of which is to evaluate several self-organizing networks in the detection of anomalies/novelties in dynamic data patterns. In this paper, we first describe three standard clustering-based approaches which use well-known self-organizing neural architectures, such ...

Kohonen’s self-organizing map (SOM) network is an unsupervised learning neural network that maps an n-dimensional input data to a lower dimensional output map while maintaining the original topological relations. The extended SOM network further groups the nodes on the output map into a user specified number of clusters. In this research effort, we applied this extended version of SOM networks ...

In this work a new clustering technique is implemented and tested. The proposed approach is based on the application of a SOM (self-organizing map) neural network and provides means to cluster U-MAT aggregated data. It relies on a flooding algorithm operating on the U-MAT and resorts to the Calinski and Harabask index to assess the depth of flooding, providing an adequate number of clusters. Th...

The self-organizing map (SOM), a biologically inspired, learning algorithm from the field of artificial neural networks, is presented as a self-organized critical (SOC) model of the extremal dynamics family. The SOM's ability to converge to an ordered configuration, independent of the initial state, is known and has been demonstrated, in the one-dimensional case. In this ordered configuration i...

This paper presents a generalized framework of a self-organizing map (SOM) applicable to more extended data classes rather than vector data. A modular structure is adopted to realize such generalization; thus, it is called a modular network SOM (mnSOM), in which each reference vector unit of a conventional SOM is replaced by a functional module. Since users can choose the functional module from...

This paper explores the combination of self-organizing map (SOM) and feedback, in order to represent sequences of inputs. In general, neural networks with time-delayed feedback represent time implicitly, by combining current inputs and past activities. It has been difficult to apply this approach to SOM, because feedback generates instability during learning. We demonstrate a solution to this p...

This study introduces the classiication of musical instrument sounds by artiicial neural networks (ANN). The time varying spectral contents of sounds are estimated based on Short-time Fourier Transform (STFT) and are applied to ANN structures for classiication. Recognition results obtained from a multilayer perceptron (MLP), time delay neural network (TDNN) and a hybrid self organizing map radi...

This paper pursues some applications of Rough Set Theory (RST) and neural-fuzzy model to analysis of "lugeon data". In the manner, using Self Organizing Map (SOM) as a pre-processing the data are scaled and then the dominant rules by RST, are elicited. Based on these rules variations of permeability in the different levels of Shivashan dam, Iran has been highlighted. Then, via using a combining...